Application Keynote Lecture
Prof. Simon Lucas (Queen Mary, University of London)
General Game AI with Statistical Forward Planning Algorithms
Statistical forward planning algorithms make use of the forward model of a game to search for action plans based on the reward profiles of the playouts. This provides powerful AI for many games (or problems in general that have fast and easily copied simulation models). Example algorithms include Monte Carlo Tree Search, and Rolling Horizon Evolution.
In this talk I will give a brief overview of the algorithms and demonstrate their ability to play a variety of games surprisingly well without the need for any prior training. I will also describe recent advances and future directions, including sample-efficient parameter tuning, learning forward models, dealing with difficult classes of games, and application to real-world problems.
Simon Lucas is a professor of Artificial Intelligence and Head of the School of Electronic Engineering and Computer Science at Queen Mary University of London where he also heads the Game AI Research Group. He holds a PhD degree (1991) in Electronics and Computer Science from the University of Southampton. He is the founding Editor-in-Chief of the IEEE Transactions on Games and co-founded the IEEE Conference on Conference on Games. His research involves developing and applying computational intelligence techniques to build better game AI, use AI to design better games, provide deep insights into the nature of intelligence and work towards Artificial General Intelligence. He is a fellow of the Alan Turing Institute.